Small neural networks invert the RG coarse-graining in the 2D Ising model to probabilistically generate critical configurations that reproduce scaling observables and nontrivial RG eigenvalues.
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Dreaming up scale invariance via inverse renormalization group
Small neural networks invert the RG coarse-graining in the 2D Ising model to probabilistically generate critical configurations that reproduce scaling observables and nontrivial RG eigenvalues.